Dynamic profiling of the protein life cycle in response to pathogens by Marko Jovanovic, Michael S. Rooney, Philipp Mertins, Dariusz Przybylski, Nicolas Chevrier, Rahul Satija, Edwin H. Rodriguez, Alexander P. Fields, Schraga Schwartz, Raktima Raychowdhury, Maxwell R. Mumbach, Thomas Eisenhaure, Michal Rabani, Dave Gennert, Diana Lu, Toni Delorey, Jonathan S. Weissman, Steven A. Carr, Nir Hacohen, and Aviv Regev Science Volume 347(6226):1259038 March 6, 2015 Published by AAAS

Dynamic protein expression regulation in dendritic cells upon stimulation with LPS. Dynamic protein expression regulation in dendritic cells upon stimulation with LPS. We developed an integrated experimental and computational strategy to quantitatively assess how protein levels are maintained in the context of a dynamic response. Our results support a model in which the induction of novel cellular functions is primarily driven through transcriptional changes, whereas regulation of protein production or degradation updates the levels of preexisting functions. Marko Jovanovic et al. Science 2015;347:1259038 Published by AAAS

Fig. 1 Framework to study the dynamic protein life cycle. Framework to study the dynamic protein life cycle. (A) The dynamic protein life cycle. (Top) RNA transcription, processing, and degradation (dashed gray box) determine mRNA levels (red), which together with per-mRNA translation (tan) and protein degradation/removal (turquoise) determine final protein levels. (Bottom) Hypothetical contribution of each process (stacked chart) to protein levels at steady state (left) or to fold changes (right, three hypothetical scenarios). (B) Experimental and analysis workflow. From top to bottom: experimental system (“Experiment”) consisted of DCs grown in medium-heavy SILAC (M) medium until LPS (top) or MOCK (bottom) stimulation, when heavy (H) SILAC is substituted. A “standard,” light (L) SILAC labeled sample is spiked in. The resulting measurements (“Data”) include M/L and H/L ratios (proxies for protein degradation/removal and production, respectively), as well as RNA-Seq data at each time point. These are used to fit the parameters of an ordinary differential equation model (“Analysis”), where R(t) = modeled mRNA change over time; T(t) and D(t) = per-mRNA translation and protein degradation rate constants over time, respectively; γ(t) = recycling (“impurity”) rate; and H(t) and M(t) = modeled change in heavy (H/L) and medium (M/L) channels, respectively. The result (“Model”) are the estimated per-mRNA translation and degradation rates over time. Details are provided in the text and (29). Marko Jovanovic et al. Science 2015;347:1259038 Published by AAAS

Fig. 2 The protein life cycle in LPS-stimulated DCs. The protein life cycle in LPS-stimulated DCs. (A) Shown are (left to right), for all 2288 genes (rows) that were quantified in all samples, mRNA expression, H/L protein expression, and M/L protein expression in LPS- and MOCK-stimulated DCs from each replicate (columns). Gene order is the same across all heatmaps and determined by means of hierarchical clustering of fitted fold changes in mRNA level, translation rate, and degradation rate. Values are median normalized by row, logged, and robust z-transformed per map (color scale). (B) Fitted differential expression of the same 2288 genes (rows). Left to right: Robust z-score fitted differential expression ratios (LPS/MOCK; red/blue color scale) for R(t), H(t), and M(t) in LPS- versus MOCK-stimulated DCs from each replicate (columns), with the log2 fold changes between LPS- and MOCK-stimulated DCs at 12 hours after stimulation for mRNA (ΔR), per-mRNA translation rate (ΔTr), and protein degradation rate (ΔDeg) (also z-scored). Rightmost column, immune response (purple), ribosomal (green), and mitochondrial (orange) proteins. Marko Jovanovic et al. Science 2015;347:1259038 Published by AAAS

Fig. 3 Contributions of mRNA levels and the protein life cycle to steady-state and dynamic protein levels. Contributions of mRNA levels and the protein life cycle to steady-state and dynamic protein levels. (A to D) Global contributions of mRNA levels (orange), translation rates (tan), and protein degradation rates (turquoise) to protein levels. Translation rates were derived either from pulsed SILAC data [(A), (C), and (D)] or from TE values from ribosome profiling data (B). Contributions to steady-state protein levels before LPS induction [(A) and (B)] or to the change in protein abundance between LPS-induced and mock-treated cells [(C) and (D)] are shown. The contributions to the fold change (C) and to the absolute change in protein abundances (D) after LPS stimulation are given. The contributions for steady state presented exclude the percent of the variance in measured protein levels that is not explained by the variance in mRNA, translation, or protein degradation (fig. S10). Per-gene parameter values were in the order 1, mRNA; 2, translation; 3, degradation (29). All possible orderings are provided in fig. S11. (E) Functional processes controlled by distinct regulatory steps. For each process (rows) and regulatory step (columns) shown are the magnitudes of the log10(P values) for the values or differential fold changes (LPS/MOCK at 12 hours) of mRNA levels, protein synthesis, or degradation rates of genes annotated to this process versus the background of all genes fit by the model. Values are signed according to directionality of the enrichment (Wilcoxon rank sum test). Shown are the five gene sets most enriched for increased or decreased rates for the three “fold change” columns, along with their scores in all six regulatory modes. Nearly redundant gene sets were removed (all gene sets are available in table S6). (F) Examples of regulation of expression dynamics. For each of three genes in each of LPS (orange) and MOCK (black) condition shown are the measured values (dots) and fits (curves) for (top to bottom) mRNA levels (in mRNA molecules), per-mRNA translation rates (protein molecules/mRNA molecule/hour), degradation rates (1 per hour), H(t), M(t), and total protein [(M+H)(t)]; x axis, time; y axis, intensity or rate. Light blue indicates key regulatory mode. mRNA and protein molecules are only proxies for transcripts per million (TPM) and intensity-based absolute quantification (IBAQ) microshares, respectively, in order to help interpretation (29). Marko Jovanovic et al. Science 2015;347:1259038 Published by AAAS

Fig. 4 Degradation of mitochondrial proteins after LPS stimulation is associated with mitophagy. Degradation of mitochondrial proteins after LPS stimulation is associated with mitophagy. (A and B) Increased translation rates of some ribosomal proteins (A) and increased degradation rates of mitochondrial proteins (B). Shown are the distributions of log2 fold changes of translation rates (ΔTi, A) or degradation rates (ΔDi, B) between LPS- and MOCK-stimulated cells of all measured ribosomal proteins [(A), red] or mitochondrial proteins [(B), red; from MitoCarta annotations (43)] and all measured proteins (gray). (C) Evidence of mitophagy in LPS-stimulated DCs. Shown is the mitochondrial to nuclear DNA ratio (y axis) in DCs at 0, 12, and 24 hours after LPS stimulation (x axis). Values are normalized to the average mitochondrial to nuclear DNA ratio at 0 hours. Asterisk indicates a significant change relative to 0 hours (P = 0.016, t test, n = 3 independent biological replicates). (D) Distribution of raw log2 LPS/MOCK M/L ratios (a proxy for protein decay) for all measured mitochondrial proteins [in MitoCarta (43)] at 12 hours (black) and 24 hours (gray) after stimulation. Marko Jovanovic et al. Science 2015;347:1259038 Published by AAAS